Metaphors' World

@inproceedings{cao-etal-2025-human,
    title = "Human-in-the-Loop Generation of Adversarial Texts: A Case Study on {T}ibetan Script",
    author = "Cao, Xi  and
      Sun, Yuan  and
      Li, Jiajun  and
      Gesang, Quzong  and
      Qun, Nuo  and
      Tashi, Nyima",
    editor = "Liu, Xuebo  and
      Purwarianti, Ayu",
    booktitle = "Proceedings of The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.ijcnlp-demo.2/",
    pages = "9--16",
    ISBN = "979-8-89176-301-2",
    abstract = "DNN-based language models excel across various NLP tasks but remain highly vulnerable to textual adversarial attacks. While adversarial text generation is crucial for NLP security, explainability, evaluation, and data augmentation, related work remains overwhelmingly English-centric, leaving the problem of constructing high-quality and sustainable adversarial robustness benchmarks for lower-resourced languages both difficult and understudied. First, method customization for lower-resourced languages is complicated due to linguistic differences and limited resources. Second, automated attacks are prone to generating invalid or ambiguous adversarial texts. Last but not least, language models continuously evolve and may be immune to parts of previously generated adversarial texts. To address these challenges, we introduce HITL-GAT, an interactive system based on a general approach to human-in-the-loop generation of adversarial texts. Additionally, we demonstrate the utility of HITL-GAT through a case study on Tibetan script, employing three customized adversarial text generation methods and establishing its first adversarial robustness benchmark, providing a valuable reference for other lower-resourced languages."
}

@INPROCEEDINGS{10889732,
  author={Cao, Xi and Gesang, Quzong and Sun, Yuan and Qun, Nuo and Nyima, Tashi},
  booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual Similarity}, 
  year={2025},
  volume={},
  number={},
  pages={1-5},
  keywords={Visualization;Perturbation methods;Semantics;Signal processing algorithms;Benchmark testing;Signal processing;Robustness;Visual databases;Speech processing;National security;Textual adversarial attack;Adversarial text generation;Adversarial robustness evaluation;Language model;Tibetan script},
  doi={10.1109/ICASSP49660.2025.10889732}}

@inproceedings{10.1145/3589335.3652503,
author = {Cao, Xi and Qun, Nuo and Gesang, Quzong and Zhu, Yulei and Nyima, Trashi},
title = {Multi-Granularity Tibetan Textual Adversarial Attack Method Based on Masked Language Model},
year = {2024},
isbn = {9798400701726},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3589335.3652503},
doi = {10.1145/3589335.3652503},
abstract = {In social media, neural network models have been applied to hate speech detection, sentiment analysis, etc., but neural network models are susceptible to adversarial attacks. For instance, in a text classification task, the attacker elaborately introduces perturbations to the original texts that hardly alter the original semantics in order to trick the model into making different predictions. By studying textual adversarial attack methods, the robustness of language models can be evaluated and then improved. Currently, most of the research in this field focuses on English, and there is also a certain amount of research on Chinese. However, there is little research targeting Chinese minority languages. With the rapid development of artificial intelligence technology and the emergence of Chinese minority language models, textual adversarial attacks become a new challenge for the information processing of Chinese minority languages. In response to this situation, we propose a multi-granularity Tibetan textual adversarial attack method based on masked language models called TSTricker. We utilize the masked language models to generate candidate substitution syllables or words, adopt the scoring mechanism to determine the substitution order, and then conduct the attack method on several fine-tuned victim models. The experimental results show that TSTricker reduces the accuracy of the classification models by more than 28.70\% and makes the classification models change the predictions of more than 90.60\% of the samples, which has an evidently higher attack effect than the baseline method.},
booktitle = {Companion Proceedings of the ACM Web Conference 2024},
pages = {1672–1680},
numpages = {9},
keywords = {language model, robustness, textual adversarial attack, tibetan},
location = {Singapore, Singapore},
series = {WWW '24}
}

@inproceedings{cao-etal-2023-pay-attention,
    title = "Pay Attention to the Robustness of {C}hinese Minority Language Models! Syllable-level Textual Adversarial Attack on {T}ibetan Script",
    author = "Cao, Xi  and
      Dawa, Dolma  and
      Qun, Nuo  and
      Nyima, Trashi",
    editor = "Ovalle, Anaelia  and
      Chang, Kai-Wei  and
      Mehrabi, Ninareh  and
      Pruksachatkun, Yada  and
      Galystan, Aram  and
      Dhamala, Jwala  and
      Verma, Apurv  and
      Cao, Trista  and
      Kumar, Anoop  and
      Gupta, Rahul",
    booktitle = "Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.trustnlp-1.4/",
    doi = "10.18653/v1/2023.trustnlp-1.4",
    pages = "35--46",
    abstract = "The textual adversarial attack refers to an attack method in which the attacker adds imperceptible perturbations to the original texts by elaborate design so that the NLP (natural language processing) model produces false judgments. This method is also used to evaluate the robustness of NLP models. Currently, most of the research in this field focuses on English, and there is also a certain amount of research on Chinese. However, to the best of our knowledge, there is little research targeting Chinese minority languages. Textual adversarial attacks are a new challenge for the information processing of Chinese minority languages. In response to this situation, we propose a Tibetan syllable-level black-box textual adversarial attack called TSAttacker based on syllable cosine distance and scoring mechanism. And then, we conduct TSAttacker on six models generated by fine-tuning two PLMs (pre-trained language models) for three downstream tasks. The experiment results show that TSAttacker is effective and generates high-quality adversarial samples. In addition, the robustness of the involved models still has much room for improvement."
}